English | Size: 1.27 GB
Genre: eLearning
Learn
Implement and infer Ordinary Least Square (OLS) regression using R
Apply statistical- and machine-learning based regression models to deal with problems such as multicollinearity
Carry out the variable selection and assess model accuracy using techniques such as cross-validation
Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier
About
With so many R Statistics and Machine Learning courses around, why enroll for this?
Regression analysis is one of the central aspects of both statistical- and machine learning-based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical, hands-on way. It explores relevant concepts in a practical way, from basic to expert level. This course can help you achieve better grades, gain new analysis tools for your academic career, implement your knowledge in a work setting, and make business forecasting-related decisions. You will go all the way from implementing and inferring simple OLS (Ordinary Least Square) regression models to dealing with issues of multicollinearity in regression to machine learning-based regression models.
Become a Regression Analysis Expert and Harness the Power of R for Your Analysis
• Get started with R and RStudio. Install these on your system, learn to load packages, and read in different types of data in R
• Carry out data cleaning and data visualization using R
• Implement Ordinary Least Square (OLS) regression in R and learn how to interpret the results.
• Learn how to deal with multicollinearity both through the variable selection and regularization techniques such as ridge regression
• Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.
• Evaluate the regression model accuracy
• Implement Generalized Linear Models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.
• Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.
• Work with tree-based machine learning models
All the code and supporting files for this course are available at –
https://github.com/PacktPublishing/Regression-Analysis-for-Statistics-and-Machine-Learning-in-R
Features
Provides in-depth training in everything you need to know to get started with practical R data science
The course will teach the student with a basic-level statistical knowledge to perform some of the most common advanced regression analysis-based techniques
Equip students to use R to perform different statistical and machine learning data analysis and visualization tasks
https://nitroflare.com/view/68C60429421FE81/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part1.rar
https://nitroflare.com/view/18B008CBD9B5766/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part2.rar
https://nitroflare.com/view/1A916D5060BC068/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part3.rar
https://nitroflare.com/view/9070B1F66CBFE84/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part4.rar
https://nitroflare.com/view/05DCC57A5AB31F8/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part5.rar
https://nitroflare.com/view/8A8F6A5FA5C75CE/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part6.rar
https://nitroflare.com/view/914DCD2BD441239/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part7.rar
https://rapidgator.net/file/fcfc5e9ee224675fae9c1370bb73d7ea/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part1.rar.html
https://rapidgator.net/file/7e6d7c5d7f745fa521f6b60a4fe3c2d0/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part2.rar.html
https://rapidgator.net/file/99282622b0354669bae71fae9d8a16f7/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part3.rar.html
https://rapidgator.net/file/5e800f7870c078da3be8645d2bc32a39/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part4.rar.html
https://rapidgator.net/file/599007943698cfd2480153384a0f48d2/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part5.rar.html
https://rapidgator.net/file/03b05286fa9ae21d642636bccaf087cd/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part6.rar.html
https://rapidgator.net/file/3b86c22c1ac4fd3ac3cef851986bffc4/PT.Regression.Analysis.for.Statistics.and.Machine.Learning.in.R.part7.rar.html
If any links die or problem unrar, send request to
https://forms.gle/e557HbjJ5vatekDV9
Leave a Reply